In MRI, the image with poor quality, especially the image with noise interference or low contrast, may provide insufficient data for the visual interpretation of the affected part. Image segmentation provides an effective method to facilitate early detection and further diagnosis. By introducing a Particle Swarm Optimization (PSO) initialization step and a novel dissimilarity measure metric, we present a local information kernelized fuzzy C‐means (LIKFCM) algorithm for image segmentation. The dissimilarity measure metric, considering an adaptive tradeoff weighted factor, incorporates the Mahalanobis distance and outliers‐rejection‐based spatial term which eliminates unreliable neighboring information. By using this dissimilarity measure metric, the new algorithm could take reliable contextual information into account and achieve better segmentation results on images with complexed boundaries. Furthermore, the adaptive tradeoff factor depends on a fast noise estimation algorithm. This factor avoids subjective adjustment and makes the LIKFCM algorithm more universal. To evaluate the performance of the proposed algorithm both quantitatively and qualitatively, experiments are conducted both on synthetic images and real‐world images with different kinds of noise. Segmentation Accuracy (SA) and Comparison scores are used to evaluate the performance of both proposed algorithm and other methods. Experimental results illustrate that the proposed algorithm has better performance on denoising and reserving useful edges. The LIKFCM algorithm not only shows more robustness to noise but also preserves the texture details of the images.

中文翻译：

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新颖的局部信息核模糊C均值算法用于图像分割

在MRI中，质量较差的图像，尤其是噪声干扰或对比度较低的图像，可能无法提供足够的数据来直观地显示患部。图像分割提供了一种有助于早期发现和进一步诊断的有效方法。通过引入粒子群优化（PSO）初始化步骤和新颖的相异性度量标准，我们提出了一种用于图像分割的局部信息核化模糊C均值（LIKFCM）算法。考虑到自适应权衡加权因子的相异性度量标准结合了Mahalanobis距离和基于异常值剔除的空间项，从而消除了不可靠的邻近信息。通过使用这种差异性度量指标，新算法可以考虑可靠的上下文信息，并在具有复杂边界的图像上获得更好的分割结果。此外，自适应权衡因子取决于快速噪声估计算法。该因素避免了主观调整，并使LIKFCM算法更具通用性。为了定量和定性地评估所提出算法的性能，在合成图像和具有不同噪声的真实世界图像上都进行了实验。分段准确性（SA）和比较分数用于评估所提出算法和其他方法的性能。实验结果表明，该算法在去噪和保留有用边缘方面具有更好的性能。